Overview

Dataset statistics

Number of variables6
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 KiB
Average record size in memory57.5 B

Variable types

Categorical2
Text1
Numeric3

Dataset

Description□ 사업명: 체육인재 장학지원사업 □ 사업내용: 학업의지가 높고 성장잠재력을 보유한 저소득층 학생선수 대상 장학금 지원 □ 지원대상: 저소득층 가정의 초·중·고 학생 선수 1,266명(2023년 기준이며 매년 변동됨) □ 지원내용 ㅇ 지원금액: 1인당 매월 40만원 이내(사용부문 한정) ㅇ 지원방식: 바우처 지원(매월 정액 포인트 지급 후 익월 정산) ㅇ지원기간: 5월 ∼ 차년도 2월 (최대 10개월) ㅇ장학금 사용부문 ① 스포츠 부문: 스포츠 용품 및 의류, 스포츠시설, 프로그램 수강료 등 ② 학업 부문: 서적 및 수업교재, 입시 및 보습학원, 체육학원 및 무술도장 등 이 사업을 통해 도출한 지역별, 성별, 학년별 체육인재 장학지원 합격률 정보를 공개합니다.
URLhttps://www.data.go.kr/data/15118798/fileData.do

Alerts

기준연도 has constant value ""Constant
지원인원 is highly overall correlated with 선정인원 and 1 other fieldsHigh correlation
선정인원 is highly overall correlated with 지원인원 and 1 other fieldsHigh correlation
조사구분 is highly overall correlated with 지원인원 and 1 other fieldsHigh correlation
조사항목 has unique valuesUnique
선정인원 has unique valuesUnique

Reproduction

Analysis started2023-12-12 05:33:38.947810
Analysis finished2023-12-12 05:33:40.390528
Duration1.44 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연도
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023
24 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 24
100.0%

Length

2023-12-12T14:33:40.472560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:33:40.567456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 24
100.0%

조사구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
지역별
17 
학년별
4 
성별
2 
총인원
 
1

Length

Max length3
Median length3
Mean length2.9166667
Min length2

Unique

Unique1 ?
Unique (%)4.2%

Sample

1st row총인원
2nd row지역별
3rd row지역별
4th row지역별
5th row지역별

Common Values

ValueCountFrequency (%)
지역별 17
70.8%
학년별 4
 
16.7%
성별 2
 
8.3%
총인원 1
 
4.2%

Length

2023-12-12T14:33:40.663294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:33:40.784473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지역별 17
70.8%
학년별 4
 
16.7%
성별 2
 
8.3%
총인원 1
 
4.2%

조사항목
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-12T14:33:41.010285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.25
Min length1

Characters and Unicode

Total characters54
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)100.0%

Sample

1st row총인원
2nd row서울
3rd row부산
4th row대구
5th row인천
ValueCountFrequency (%)
총인원 1
 
4.2%
서울 1
 
4.2%
고등학교 1
 
4.2%
중학교 1
 
4.2%
초등학교 1
 
4.2%
여 1
 
4.2%
남 1
 
4.2%
제주 1
 
4.2%
경남 1
 
4.2%
경북 1
 
4.2%
Other values (14) 14
58.3%
2023-12-12T14:33:41.381116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 4
 
7.4%
교 4
 
7.4%
남 4
 
7.4%
전 3
 
5.6%
경 3
 
5.6%
북 3
 
5.6%
산 2
 
3.7%
대 2
 
3.7%
울 2
 
3.7%
주 2
 
3.7%
Other values (21) 25
46.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 54
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 4
 
7.4%
교 4
 
7.4%
남 4
 
7.4%
전 3
 
5.6%
경 3
 
5.6%
북 3
 
5.6%
산 2
 
3.7%
대 2
 
3.7%
울 2
 
3.7%
주 2
 
3.7%
Other values (21) 25
46.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 54
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 4
 
7.4%
교 4
 
7.4%
남 4
 
7.4%
전 3
 
5.6%
경 3
 
5.6%
북 3
 
5.6%
산 2
 
3.7%
대 2
 
3.7%
울 2
 
3.7%
주 2
 
3.7%
Other values (21) 25
46.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 54
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 4
 
7.4%
교 4
 
7.4%
남 4
 
7.4%
전 3
 
5.6%
경 3
 
5.6%
북 3
 
5.6%
산 2
 
3.7%
대 2
 
3.7%
울 2
 
3.7%
주 2
 
3.7%
Other values (21) 25
46.3%

지원인원
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean301.70833
Minimum4
Maximum1813
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T14:33:41.522415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile31
Q169.25
median131
Q3287.25
95-th percentile1186.1
Maximum1813
Range1809
Interquartile range (IQR)218

Descriptive statistics

Standard deviation438.2535
Coefficient of variation (CV)1.4525734
Kurtosis6.0265168
Mean301.70833
Median Absolute Deviation (MAD)88
Skewness2.4301137
Sum7241
Variance192066.13
MonotonicityNot monotonic
2023-12-12T14:33:41.681377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
131 2
 
8.3%
31 2
 
8.3%
1813 1
 
4.2%
207 1
 
4.2%
35 1
 
4.2%
756 1
 
4.2%
687 1
 
4.2%
336 1
 
4.2%
539 1
 
4.2%
1262 1
 
4.2%
Other values (12) 12
50.0%
ValueCountFrequency (%)
4 1
4.2%
31 2
8.3%
35 1
4.2%
40 1
4.2%
46 1
4.2%
77 1
4.2%
78 1
4.2%
80 1
4.2%
87 1
4.2%
109 1
4.2%
ValueCountFrequency (%)
1813 1
4.2%
1262 1
4.2%
756 1
4.2%
687 1
4.2%
539 1
4.2%
336 1
4.2%
271 1
4.2%
207 1
4.2%
171 1
4.2%
169 1
4.2%

선정인원
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211
Minimum3
Maximum1266
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T14:33:41.834388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile20.5
Q153.75
median101
Q3165.5
95-th percentile814.85
Maximum1266
Range1263
Interquartile range (IQR)111.75

Descriptive statistics

Standard deviation306.40964
Coefficient of variation (CV)1.4521784
Kurtosis5.8362323
Mean211
Median Absolute Deviation (MAD)60.5
Skewness2.4082828
Sum5064
Variance93886.87
MonotonicityNot monotonic
2023-12-12T14:33:41.984478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1266 1
 
4.2%
110 1
 
4.2%
30 1
 
4.2%
593 1
 
4.2%
470 1
 
4.2%
173 1
 
4.2%
412 1
 
4.2%
854 1
 
4.2%
29 1
 
4.2%
59 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
3 1
4.2%
19 1
4.2%
29 1
4.2%
30 1
4.2%
40 1
4.2%
41 1
4.2%
58 1
4.2%
59 1
4.2%
61 1
4.2%
68 1
4.2%
ValueCountFrequency (%)
1266 1
4.2%
854 1
4.2%
593 1
4.2%
470 1
4.2%
412 1
4.2%
173 1
4.2%
163 1
4.2%
117 1
4.2%
116 1
4.2%
110 1
4.2%

합격률
Real number (ℝ)

Distinct19
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.583333
Minimum51
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T14:33:42.103116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile54.05
Q168
median71.5
Q379
95-th percentile93.25
Maximum100
Range49
Interquartile range (IQR)11

Descriptive statistics

Standard deviation12.021418
Coefficient of variation (CV)0.16337148
Kurtosis0.066951761
Mean73.583333
Median Absolute Deviation (MAD)5.5
Skewness0.28390006
Sum1766
Variance144.51449
MonotonicityNot monotonic
2023-12-12T14:33:42.248952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
68 4
16.7%
70 2
 
8.3%
78 2
 
8.3%
100 1
 
4.2%
86 1
 
4.2%
51 1
 
4.2%
76 1
 
4.2%
94 1
 
4.2%
89 1
 
4.2%
74 1
 
4.2%
Other values (9) 9
37.5%
ValueCountFrequency (%)
51 1
 
4.2%
53 1
 
4.2%
60 1
 
4.2%
61 1
 
4.2%
67 1
 
4.2%
68 4
16.7%
69 1
 
4.2%
70 2
8.3%
73 1
 
4.2%
74 1
 
4.2%
ValueCountFrequency (%)
100 1
4.2%
94 1
4.2%
89 1
4.2%
88 1
4.2%
86 1
4.2%
82 1
4.2%
78 2
8.3%
76 1
4.2%
75 1
4.2%
74 1
4.2%

Interactions

2023-12-12T14:33:39.850088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:39.184201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:39.541028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:39.957991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:39.304497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:39.650667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:40.065851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:39.447725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:33:39.754885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:33:42.340166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조사구분조사항목지원인원선정인원합격률
조사구분1.0001.0000.9140.9190.000
조사항목1.0001.0001.0001.0001.000
지원인원0.9141.0001.0000.9700.000
선정인원0.9191.0000.9701.0000.000
합격률0.0001.0000.0000.0001.000
2023-12-12T14:33:42.445017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지원인원선정인원합격률조사구분
지원인원1.0000.987-0.4710.812
선정인원0.9871.000-0.4050.765
합격률-0.471-0.4051.0000.000
조사구분0.8120.7650.0001.000

Missing values

2023-12-12T14:33:40.197487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:33:40.345732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

기준연도조사구분조사항목지원인원선정인원합격률
02023총인원총인원1813126670
12023지역별서울17111668
22023지역별부산16911769
32023지역별대구13110782
42023지역별인천786178
52023지역별광주805873
62023지역별대전776888
72023지역별울산311961
82023지역별세종4375
92023지역별경기27116360
기준연도조사구분조사항목지원인원선정인원합격률
142023지역별전남1319774
152023지역별경북464189
162023지역별경남875968
172023지역별제주312994
182023성별남126285468
192023성별여53941276
202023학년별초등학교33617351
212023학년별중학교68747068
222023학년별고등학교75659378
232023학년별특수학교353086